HWT-Adwin is a new Hoeffding Window Tree, while HAT evolves from HWT and replaces, at each node, the original counter with an Adwin instance. Adwin instances are also change detectors, so they notice when a change in the statistics at that node is detected, which can also be a possible concept change. Gomes et al. [1]} propose a technique called Adaptive Random Forest (ARF). It is the adaptation of Random Forest (RF) algorithm [2]} to work with streaming data. RF combines multiple hypothesis from multiple decision trees in order to form a final better one. It grows a lot of decision trees at training time and gives in output the most popular class. To avoid overfitting, the idea is to combine bootstrap aggregating and feature bagging together. Bootstrap aggregating repeatedly selects a random sample with replacement of the training set \(b\)  times and grows trees with these new samples, i.e. one tree for each sample. Feature bagging uses a random subset of the features to make the split on each tree. ARF uses the Online Bagging procedure instead of Bootstrap aggregating. Online Bagging sends \(k\)  copies of each new examples to update each model, where \(k\)  is a suitable Poisson random variable. Moreover ARF also uses Adwin to detect warnings and create “background” trees that are trained along the ensemble without influencing the ensemble predictions. If a drift is detected from the tree originating the warning signal, the original tree is then replaced by its respective background tree. Finally, we would like to mention Swt [3]} by Biffet et al. This is an algorithm that uses a meta strategy to build meta instances by increasing the original input attributes adding attributes with the values of the most recent class labels from previous samples.
